#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: BSD-3 (https://tldrlegal.com/license/bsd-3-clause-license-(revised))
# Copyright (c) 2016-2021, Cabral, Juan; Luczywo, Nadia
# Copyright (c) 2022, 2023, 2024 QuatroPe
# All rights reserved.
# =============================================================================
# DOCS
# =============================================================================
"""Stats helper for the DecisionMatrix object."""
# =============================================================================
# IMPORTS
# =============================================================================k
from ..utils import AccessorABC
# =============================================================================
# STATS ACCESSOR
# =============================================================================
[docs]
class DecisionMatrixStatsAccessor(AccessorABC):
"""Calculate basic statistics of the decision matrix.
Kind of statistic to produce:
- 'corr' : Compute pairwise correlation of columns, excluding
NA/null values.
- 'cov' : Compute pairwise covariance of columns, excluding NA/null
values.
- 'describe' : Generate descriptive statistics.
- 'kurtosis' : Return unbiased kurtosis over requested axis.
- 'mad' : Return the mean absolute deviation of the values over the
requested axis.
- 'max' : Return the maximum of the values over the requested axis.
- 'mean' : Return the mean of the values over the requested axis.
- 'median' : Return the median of the values over the requested
axis.
- 'min' : Return the minimum of the values over the requested axis.
- 'pct_change' : Percentage change between the current and a prior
element.
- 'quantile' : Return values at the given quantile over requested
axis.
- 'sem' : Return unbiased standard error of the mean over requested
axis.
- 'skew' : Return unbiased skew over requested axis.
- 'std' : Return sample standard deviation over requested axis.
- 'var' : Return unbiased variance over requested axis.
"""
# The list of methods that can be accessed of the subjacent dataframe.
_DF_WHITELIST = (
"corr",
"cov",
"describe",
"kurtosis",
"max",
"mean",
"median",
"min",
"pct_change",
"quantile",
"sem",
"skew",
"std",
"var",
)
_default_kind = "describe"
def __init__(self, dm):
self._dm = dm
def __getattr__(self, a):
"""x.__getattr__(a) <==> x.a <==> getattr(x, "a")."""
if a not in self._DF_WHITELIST:
raise AttributeError(a)
return getattr(self._dm._data_df, a)
def __dir__(self):
"""x.__dir__() <==> dir(x)."""
return super().__dir__() + [
e for e in dir(self._dm._data_df) if e in self._DF_WHITELIST
]
[docs]
def mad(self, axis=0, skipna=True):
"""Return the mean absolute deviation of the values over a given axis.
Parameters
----------
axis : int
Axis for the function to be applied on.
skipna : bool, default True
Exclude NA/null values when computing the result.
"""
df = self._dm._data_df
return (df - df.mean(axis=axis)).abs().mean(axis=axis, skipna=skipna)